Along with the development of electronic information technology and the popularity of networking, various image collecting devices are widely used in daily life to acquire a large amount of image and video data. To analyze the acquired data quickly and intelligently has become an urgent need in many fields. Therefore, image processing technology has become a hotspot of research. As an essential step in image processing, feature extraction technology has a direct impact on the final performance of the system and has attracted much research interests in recent years. Typically, features include color features, texture features, shape features, and spatial features, etc. Feature extraction is referred to as a method for representing an image block with a multi-dimensional feature vector, and it is used for subsequent processing such as image recognition, etc. Along with the continuous development of the feature extraction technology, the extraction of color features has not been limited to only the shape features of grayscale images; the extraction of multi-color features has also been gradually proposed.
In the prior art, the extraction of color features comprises: first converting an original image into sub-images corresponding to each channel in a color space, dividing each of the sub-images into cells with identical size, and after calculating the color histogram of each cell, taking each cell as the central cell to calculate the similarity values of the color histogram of the central cell and that of each neighboring cell of the central cell, determining the feature vector of each cell according to the calculated similarity value; then concatenating the feature vectors of all the cells in each sub-image to obtain the feature vectors of the sub-image; and finally concatenating the feature vectors of all the sub-images to obtain the feature vector of the whole image.
During the process of realizing the present invention, the inventors found at least the following problems in the prior art:
In prior art, when color features are extracted, the feature vector of each cell is determined by taking said each cell as the center and calculating the similarity values of the color histograms of each cell and that of each neighboring cell of the central cell. The resultant feature vector of the whole image obtained afterwards tends to yield low presentation ability, and exhibit inconsistencies in the presence of the appearance variation of the expressed object. Such deficiencies in feature extraction will result in poor effects in the subsequent feature vector based processing such as image recognition.